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Article
Accurate Automated Apnea Analysis in Preterm Infants
American Journal of Perinatology
  • Brooke D. Vergales
  • Alix O. Paget-Brown
  • Hoshik Lee, William & Mary
  • et al., et al.
  • John B. Delos, William & Mary
Document Type
Article
Department/Program
Physics
Pub Date
2-1-2014
Abstract

Objective In 2006 the apnea of prematurity (AOP) consensus group identified inaccurate counting of apnea episodes as a major barrier to progress in AOP research. We compare nursing records of AOP to events detected by a clinically validated computer algorithm that detects apnea from standard bedside monitors.

Study Design Waveform, vital sign, and alarm data were collected continuously from all very low-birth-weight infants admitted over a 25-month period, analyzed for central apnea, bradycardia, and desaturation (ABD) events, and compared with nursing documentation collected from charts. Our algorithm defined apnea as>10seconds if accompanied by bradycardia and desaturation.

Results Of the 3,019 nurse-recorded events, only 68% had any algorithm-detected ABD event. Of the 5,275 algorithm-detected prolonged apnea events>30seconds, only 26% had nurse-recorded documentation within 1 hour. Monitor alarms sounded in only 74% of events of algorithm-detected prolonged apnea events>10 seconds. There were 8,190,418 monitor alarms of any description throughout the neonatal intensive care unit during the 747 days analyzed, or one alarm every 2 to 3 minutes per nurse.

Conclusion An automated computer algorithm for continuous ABD quantitation is a far more reliable tool than the medical record to address the important research questions identified by the 2006 AOP consensus group.

DOI
https://doi.org/10.1055/s-0033-1343769
Citation Information
Brooke D. Vergales, Alix O. Paget-Brown, Hoshik Lee, et al., et al.. "Accurate Automated Apnea Analysis in Preterm Infants" American Journal of Perinatology Vol. 31 Iss. 2 (2014) p. 157 - 162
Available at: http://works.bepress.com/john-delos/8/